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Dr. Vikas Singla, Rakesh Singla, Ajay Kumar, Gurpreet Singh
ABSTRACT:In Mobile Adhoc networks, number of researchers worked on performance of the network on basis of various QoS service parameters. In case of Mobile Adhoc network, nodes involved in communication are wireless nodes having limited battery life. This battery of nodes got exhausted in communication of packets in the network. In case of MANET, battery of those nodes which are not communicating packets at the moment also got exhausted as they are part of transmission of packets of other nodes. Thus, there is need of effective utilization of energy of nodes in the network. Number of techniques has been proposed by researchers for effective energy utilization such as power management at the level of MAC layer, managing residual energy of nodes at Network layer level etc. Clustering mechanism provides scalability and topology management in hefty and impenetrable network. Weighted clustering algorithm selects cluster head on the basis of minimum weight value of various parameters. In this paper, threshold based weighted clustering approach will be provided for efficient energy utilization and lifetime enhancement of nodes of mobile ad-hoc network. Performance of threshold based weighted clustering scheme will be analyzed with LEACH approach for energy consumptio n and packet delivery ratio.
KEYWORDS:WEIGHTED CLUSTERING,LEACH,PDR,ENERGY,MANET.
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1.
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NTRODUCTIONAd-hoc Network is infrastructure less kind of network which consists of communicating nodes those can interconnect without any pre-existing infrastructure. This kind of network is called as Mobile Ad-hoc Network, when nodes of ad-hoc network are mobile in nature. Main issue in MANET is that each node of network is required to keep the information for proper routing of data among nodes of the network. Reactive, Proactive and Hybrid routing protocols are available for routing of packets in the network [3, 5, 6]. Due to mobile nature of nodes of network, these networks are having dynamic topology. Routing protocols are required to be having the capability to cope with changing topology of the network. In case of large network for routing protocol managing the number of links with dynamic topology of network to be dealt with is a big issue. Clustering has been used in case of these hefty and impenetrable network situations, by dividing the whole network into small subgroups of nodes. These subgroups of nodes are known as clusters of the network. In each cluster there is one head node defined which has been called as cluster head. Other nodes of the cluster to be in contact with the cluster head node for routing traffic within the cluster. For routing the traffic in other clusters, nodes of the cluster to be in contact with parent cluster head and respective cluster head to be in touch with other cluster head node for routing of traffic. As nodes of the network are moving in and out of cluster due to dynamic nature of network, clustering process becomes somewhat thorny. Due to this, cluster maintenance is required to be performed in the network with precise interval of time [2, 8, 11].
2. RELATED WORK
2.1. Distributed clustering Approach
In this approach, as suggested by Younis O. and Fahmy S., (2004), network at habitual intervals decides the cluster heads on the basis of residual energy and degree of node i.e. closeness to its neighbouring nodes. Here, it has been assumed that nodes are location unaware and having the equal importance in the network. This approach provides the cluster which can be effortlessly tuned for optimized resource utilization as per requirement [1].
2.2. Weighted Clustering Approach
In this algorithm as discussed by Wang Y. and Bao F. S. (2007), cluster head has been decided on the basis of weighted sum of parameters i.e. number of member nodes of the cluster, summation of distance with other neighbouring nodes, average node movement speed and rate of occurrence of node having the cluster head. High mobility of nodes in Mobile Adhoc Network caused the nodes to frequently changing their clusters that caused additional overhead [2].
2.3. Virtual Link Weight based Clustering Approach
In this approach Karimi A. et. al., (2014), suggested weight based clustering technique for enhancing the stability of cluster. Proposed mechanism compute the weight of virtual links of nodes. This computed weight has been used for computation of final weight of node and node having uppermost weight value has been chosen as cluster head. Here, weight of the nodes has been determined on the basis of neighbouring nodes whereas in universal weighted clustering approach, weight has been computed only on the basis of nodes own features [9].
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Dr. Vikas Singla, Rakesh Singla, Ajay Kumar, Gurpreet Singh
Assistant Professor, Department of Information Technology, Malout Institute of Management & Information Technology, Malout [email protected]
Lecturer, Department of Information Technology, Baba Hira Singh Bhattal Institute of Engg. & Technology(Poly. Wing), Lehragaga
Lecturer, Department of Electronics & Communication Engg., Shaheed Bhagat Singh State Technical Campus (Poly. Wing), Ferozepur.
3.
THRESHOLD
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WEIGHTED
CLUSTERING APPROACH
3.1. LEACH protocol
LEACH is hierarchical clustering protocol for energy optimization to enhance the lifetime of network. This protocol optimize the life time of network by means of rotation of cluster head using some random number to avoid the drainage of battery of cluster head. This protocol evenly distributes the energy load of cluster head among all the nodes within the cluster. LEACH protocol uses the TDMA scheduling mechanism for communication of data among nodes within the cluster and among the clusters. By using this, it tries to avoid collision in the network. LEACH protocol as its name indicates is self adaptive protocol, which efficiently uses the available bandwidth and provides low latency in addition to the energy efficiency. This protocol consists of two phases. First phase is the Set-up Clusters phase, which is for the cluster formation and selection of cluster head. Second phase is the Steady data transmission phase where data frames are transmitted by the nodes to the cluster head and to the base station [7, 9].
3.2. Proposed Technique
In the proposed method, determination of cluster head is performed by means of by selecting average of energy of all the nodes as threshold value. For selecting the cluster head, only the nodes with value greater than threshold value can participate in the selection of Cluster head. In the proposed mechanism, a counter has been set so can acts as cluster head only for specified period of time, according to counter value. Counter value has been decreased with
time. When counter reaches Zero, it means now it is the time for re-selection of cluster head node. Thus, residual energy of all the nodes of the cluster is re-computed. In the proposed approach threshold value is not fixed and dynamic in nature with respect to behaviour of network. Whenever counter reaches zero, threshold value is also updated by computing average of residual energy of nodes. Dynamic nature of threshold value is improvement over earlier techniques. Initially node in cluster with highest residual energy acts as the cluster head. After the specific period with respect to counter value, node with next highest residual energy level has been selected as cluster head. Other improvement provided by the proposed technique over earlier base protocol is that when the value of Cluster head node falls below the threshold value, how the protocol acts. In proposed approach, in the meanwhile the case, if the value of Cluster head node falls below the threshold value, then the cluster head selection procedure is re-initiated without waiting for counter value reaches to zero. This reduces the chances of cluster head node energy to fall below some specified value [8, 11].
4. RESULTS & DISCUSSIONS
Analysis of proposed technique has performed with the base protocol i.e. LEACH protocol in the current work. Experiments have been performed by implementing said techniques in NS-2 simulator. Performance of the network has been analyzed for PDR and percentage energy consumption as the network becomes more hefty i.e. by increasing number of nodes as 200, 250, 300, 350 and 400 nodes. Simulation scenario for said evaluation has been specified in table-1.
TABLE-1: SIMULATION SCENARIO
Parameter Value
# of nodes 200, 250, 300, 350, 400
Maximum speed 20 m/s
Pause time 20 s
Environment size 1000 x1000 m Packet size 512 Bytes
Traffic type CBR, TCP
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Figure 4.1: PDR vs Number of nodes for CBR Traffic
When evaluated for TCP traffic as depicted in figure 4.2, it has been noticed that PDR has been increased with network becomes more dense. It has been noted that
proposed method provides somewhat less improvement in PDR w.r.t TCP traffic whatever has been provided while evaluating w.r.t. CBR traffic.
Figure 4.2: PDR vs Number of nodes for TCP Traffic
4.2 Percentage energy consumption of proposed techniques and LEACH protocol
While evaluating for percentage energy consumption, it has been noticed from the evaluation of proposed technique
Figure 4.3: Percentage Energy Consumption vs Number of nodes for CBR Traffic
When evaluated for TCP traffic for percentage energy consumption as shown in figure 4.4, it has been noticed that somewhat higher energy has been consumed in case TCP traffic as compared to CBR traffic which is due to flow of additional acknowledgment packets in the network in
case of TCP traffic. However, it has been noticed that with respect to varying nodes, percentage energy consumption of proposed technique in case of TCP traffic pattern remains almost same.
Figure 4.4: Percentage Energy Consumption vs Number of nodes for TCP Traffic
5. CONCLUSION & FUTURE SCOPE
In this paper, threshold based weighted clustering technique has been proposed. In Proposed technique
3566 Conference on Wireless and Mobile Computing,
Networking and Communications (WiMob 2007), White Plains, NY, 2007.
[3] Singla V., Singla R., Kumar A. (2009), “Performance Evaluation and Simulation of Mobile Ad-hoc Network Routing Protocols”, International Journal of Engineering and Information Technology, Volume 1, No. 1, October 2009.
[4] Ghosekar P., Katkar G., Ghorpade P. (2010), “Mobile Ad Hoc Networking: Imperatives and Challenges”, IJCA Special Issue on Mobile Ad-hoc Networks, 2010, pp. 153-158.
[5] Singla V., Kakkar P. (2010), “Traffic Pattern based performance comparison of Reactive and Proactive protocols of Mobile Ad-hoc Networks”, International Journal of Computer Applications 5(10), August 2010. pp.16-20.
[6] Kumar A., Singla A. K. (2011), “Performance evaluation of MANET routing protocols on the basis of TCP traffic pattern”, International Journal of Information Technology Convergence and Services (IJITCS) Vol. 1, No.5, October 2011, pp. 41-48. [7] Pragati, Nath R. (2012), “Performance Evaluation of
AODV, LEACH & TORA Protocols through Simulation”, International Journal of Advanced Research in Computer Science and Software Engineering. 2(7). ISSN 2277-128X, pp. 84-89. [8] Narayanan A. E., Devi R., Jayakumar A. V. (2013),
“An Energy Efficient Cluster Head Selection for Fault tolerant routing in MANET”, International Journal of Engineering and Technology (IJET). 5(2), pp.1281-1289. ISSN 0975-4024.
[9] Karimi A., Afsharfarnia A., Zarafshan F., Al-Haddad S. A. R. (2014), “A Novel Clustering Algorithm for Mobile Ad Hoc Networks Based on Determination of Virtual Links’ Weight to Increase Network Stability”, The Scientific World Journal. 2014. Article ID 432952.
[10] Shakya P., Sharma V., Saroliya A. (2015), “Enhanced multipath LEACH protocol for increasing network life time and minimizing overhead in MANET”, IEEE International Conference on Communication Networks (ICCN), Gwalior, pp.148-154.
